At Tulza, we help you build, manage and scale your Private AI efforts with data engineering best practices and open weight models on infrastructure you control.
Start with a Data Stack Audit and build toward AI capabilities for your customers.
Your data should strengthen your competitive advantage, not train someone else’s models.
Open weight models deployed on your infrastructure with your controls.
Governed, observable and reliable data foundations for analytics and AI workloads.
Transfer learning and distillation tuned to your internal data and workflows.
Secure deployment patterns, access controls and operational rigor from day one.
Start with an audit, then move toward a scalable private AI platform built on strong data systems.
Assess your current data environment and define a practical roadmap to AI readiness.
Deploy open weight LLMs on your hardware so your data stays inside your perimeter.
Fine-tune and distill on private domain data to match your business context and use cases.
Build reliable ingestion, validation and transformation pipelines for feature-ready datasets.
Design modern systems optimized for analytics, governance, performance and AI workloads.
Apply lineage, controls, policies and secure deployment practices that scale with your platform.
A few examples of production-grade data systems delivered in the field.
Scalable pipelines for electricity market data
Built and operated complex data pipelines for diversified strategy research across multiple market horizons.
Unified near real-time energy datasets
Delivered integrated datasets and operational analytics to support forecasting and grid optimisation.
Tell us about your current data environment and the private AI capabilities you want to build.